文献类型：期刊文献

英文题名：A dimensionally augmented and physics-informed machine learning for quality prediction of additively manufactured high-entropy alloy

作者：Wang, Haijie[1];Li, Bo[1];Xuan, Fu-Zhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China;[3]Shanghai Collaborat Innovat Ctr High End Equipmen, Shanghai 200237, Peoples R China

年份：2022

卷号：307

外文期刊名：JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

收录：;EI(收录号：20222412220785);WOS:【SCI-EXPANDED(收录号:WOS:000812883800002)】；

基金：Acknowledgments This research work is sponsored by National Natural Science Foun-dation of China (No.52175140) , International Collaboration Program from Science and Technology Commission of Shanghai Municipality in China (No.19110712500) , Natural Science Foundation of Shanghai in China (No.20ZR1414000) , and the Fundamental Research Funds for the Central Universities in China.

语种：英文

外文关键词：Machine learning; Additive manufacturing; Quality characteristics prediction; Dimensionality augmentation; Physical information

摘要：Selective laser melting (SLM) additive manufacturing (AM) is widely used due to its significant advantages in designing and manufacturing special-shaped complex components. The process parameters of SLM determine the quality of as-built parts, but it is difficult to establish an accurate and reliable mathematical model to connect process parameters with the quality of as-built parts. However, data-driven machine learning can effectively solve the analysis and prediction problem of complex process. Therefore, a machine learning (ML) prediction method based on dimensionality augmentation and physical information is proposed, which connects the process parameters (laser power, hatching space, scanning speed, and layer thickness) of SLM with the quality characteristics (top layer surface roughness and relative density) of as-built parts. The four process parameter features (4-dimensional features) are expanded to high-dimensional features through feature engineering to characterize the quality of as-built parts. In addition, the physical information of powder melting forming in SLM process is fused with ML algorithm, the theory-guided ML is used to improve the prediction accuracy of the model. In this paper, the CoCrFeNiMn high-entropy alloy as-built samples dataset is used for network training of four ML algorithms, and three assessment indexes are used to evaluate the prediction model. The results show that dimensionally augmented and physics-informed ML model has better prediction accuracy and generalization ability. The proposed method can also provide guidance for optimizing process parameters.